Overview

Dataset statistics

Number of variables12
Number of observations661
Missing cells641
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.1 KiB
Average record size in memory96.2 B

Variable types

Categorical1
Numeric11

Alerts

Code has a high cardinality: 661 distinct values High cardinality
QD_M is highly correlated with QD_PS and 6 other fieldsHigh correlation
QD_PS is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_L is highly correlated with QD_M and 7 other fieldsHigh correlation
QD_EH is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_R is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Com is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Soc is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Aut is highly correlated with QD_M and 6 other fieldsHigh correlation
ADIR_Soc is highly correlated with QD_LHigh correlation
QD_M is highly correlated with QD_PS and 6 other fieldsHigh correlation
QD_PS is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_L is highly correlated with QD_M and 7 other fieldsHigh correlation
QD_EH is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_R is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Com is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Soc is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Aut is highly correlated with QD_M and 6 other fieldsHigh correlation
ADIR_Soc is highly correlated with QD_LHigh correlation
QD_M is highly correlated with QD_PS and 1 other fieldsHigh correlation
QD_PS is highly correlated with QD_M and 5 other fieldsHigh correlation
QD_L is highly correlated with QD_PS and 4 other fieldsHigh correlation
QD_EH is highly correlated with QD_PS and 3 other fieldsHigh correlation
QD_R is highly correlated with QD_M and 3 other fieldsHigh correlation
VABS_Com is highly correlated with QD_PS and 4 other fieldsHigh correlation
VABS_Soc is highly correlated with QD_PS and 3 other fieldsHigh correlation
VABS_Aut is highly correlated with VABS_Com and 1 other fieldsHigh correlation
QD_M is highly correlated with QD_PS and 6 other fieldsHigh correlation
QD_PS is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_L is highly correlated with QD_M and 7 other fieldsHigh correlation
QD_EH is highly correlated with QD_M and 6 other fieldsHigh correlation
QD_R is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Com is highly correlated with QD_M and 7 other fieldsHigh correlation
VABS_Soc is highly correlated with QD_M and 6 other fieldsHigh correlation
VABS_Aut is highly correlated with QD_M and 6 other fieldsHigh correlation
ADIR_Soc is highly correlated with QD_L and 1 other fieldsHigh correlation
QD_M has 33 (5.0%) missing values Missing
QD_PS has 33 (5.0%) missing values Missing
QD_L has 21 (3.2%) missing values Missing
QD_EH has 33 (5.0%) missing values Missing
QD_R has 33 (5.0%) missing values Missing
VABS_Com has 137 (20.7%) missing values Missing
VABS_Soc has 135 (20.4%) missing values Missing
VABS_Aut has 137 (20.7%) missing values Missing
ADIR_Soc has 23 (3.5%) missing values Missing
ADIR_RRB has 24 (3.6%) missing values Missing
ADIR_AbDev has 32 (4.8%) missing values Missing
Code is uniformly distributed Uniform
Code has unique values Unique
QD_L has 12 (1.8%) zeros Zeros
ADIR_AbDev has 20 (3.0%) zeros Zeros

Reproduction

Analysis started2022-12-16 17:32:30.415681
Analysis finished2022-12-16 17:32:50.331551
Duration19.92 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct661
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
AA1
 
1
AA650
 
1
AA705
 
1
AA548
 
1
AA561
 
1
Other values (656)
656 

Length

Max length5
Median length5
Mean length4.907715582
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique661 ?
Unique (%)100.0%

Sample

1st rowAA1
2nd rowAA34
3rd rowAA36
4th rowAA72
5th rowAA91

Common Values

ValueCountFrequency (%)
AA11
 
0.2%
AA6501
 
0.2%
AA7051
 
0.2%
AA5481
 
0.2%
AA5611
 
0.2%
AA5661
 
0.2%
AA6131
 
0.2%
AA6351
 
0.2%
AA5831
 
0.2%
AA5931
 
0.2%
Other values (651)651
98.5%

Length

2022-12-16T17:32:50.419205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa11
 
0.2%
aa1661
 
0.2%
aa361
 
0.2%
aa721
 
0.2%
aa911
 
0.2%
aa21
 
0.2%
aa31
 
0.2%
aa61
 
0.2%
aa71
 
0.2%
aa81
 
0.2%
Other values (651)651
98.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

QD_M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct100
Distinct (%)15.9%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean87.17117834
Minimum28
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:50.565182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile50
Q174
median89
Q3101
95-th percentile118.65
Maximum157
Range129
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.60147594
Coefficient of variation (CV)0.2363335718
Kurtosis0.04931459745
Mean87.17117834
Median Absolute Deviation (MAD)13
Skewness-0.1979370973
Sum54743.5
Variance424.4208107
MonotonicityNot monotonic
2022-12-16T17:32:50.732864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9820
 
3.0%
8318
 
2.7%
10017
 
2.6%
8116
 
2.4%
9014
 
2.1%
9114
 
2.1%
9314
 
2.1%
8814
 
2.1%
8413
 
2.0%
10113
 
2.0%
Other values (90)475
71.9%
(Missing)33
 
5.0%
ValueCountFrequency (%)
281
 
0.2%
291
 
0.2%
353
0.5%
371
 
0.2%
382
 
0.3%
391
 
0.2%
402
 
0.3%
412
 
0.3%
436
0.9%
441
 
0.2%
ValueCountFrequency (%)
1571
0.2%
1511
0.2%
1391
0.2%
1351
0.2%
1332
0.3%
1321
0.2%
1302
0.3%
1292
0.3%
1281
0.2%
1271
0.2%

QD_PS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct103
Distinct (%)16.4%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean70.34872611
Minimum19
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:50.897717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile34
Q155
median71
Q386
95-th percentile104
Maximum161
Range142
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.22794515
Coefficient of variation (CV)0.3159679837
Kurtosis0.1057248385
Mean70.34872611
Median Absolute Deviation (MAD)16
Skewness0.1665767107
Sum44179
Variance494.0815454
MonotonicityNot monotonic
2022-12-16T17:32:51.052282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7319
 
2.9%
8115
 
2.3%
5615
 
2.3%
8013
 
2.0%
7813
 
2.0%
4813
 
2.0%
6313
 
2.0%
7113
 
2.0%
9612
 
1.8%
6712
 
1.8%
Other values (93)490
74.1%
(Missing)33
 
5.0%
ValueCountFrequency (%)
191
 
0.2%
212
0.3%
222
0.3%
232
0.3%
23.51
 
0.2%
261
 
0.2%
272
0.3%
283
0.5%
292
0.3%
301
 
0.2%
ValueCountFrequency (%)
1611
0.2%
1551
0.2%
1461
0.2%
1371
0.2%
1231
0.2%
1211
0.2%
1181
0.2%
1161
0.2%
1151
0.2%
1142
0.3%

QD_L
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct126
Distinct (%)19.7%
Missing21
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean63.0078125
Minimum0
Maximum158
Zeros12
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:51.220488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median61
Q389
95-th percentile111.05
Maximum158
Range158
Interquartile range (IQR)52

Descriptive statistics

Standard deviation32.09359993
Coefficient of variation (CV)0.5093590565
Kurtosis-0.6938688201
Mean63.0078125
Median Absolute Deviation (MAD)26
Skewness0.202422355
Sum40325
Variance1029.999156
MonotonicityNot monotonic
2022-12-16T17:32:51.377627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3014
 
2.1%
7612
 
1.8%
012
 
1.8%
5012
 
1.8%
6111
 
1.7%
9211
 
1.7%
4211
 
1.7%
7210
 
1.5%
7910
 
1.5%
4410
 
1.5%
Other values (116)527
79.7%
(Missing)21
 
3.2%
ValueCountFrequency (%)
012
1.8%
81
 
0.2%
101
 
0.2%
112
 
0.3%
122
 
0.3%
12.51
 
0.2%
134
 
0.6%
142
 
0.3%
152
 
0.3%
161
 
0.2%
ValueCountFrequency (%)
1581
0.2%
1521
0.2%
1501
0.2%
1491
0.2%
1441
0.2%
1401
0.2%
1382
0.3%
1361
0.2%
1311
0.2%
1281
0.2%

QD_EH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct114
Distinct (%)18.2%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean74.62340764
Minimum15
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:51.544084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32.35
Q154
median77
Q394.25
95-th percentile113.65
Maximum150
Range135
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation25.98411323
Coefficient of variation (CV)0.3482032521
Kurtosis-0.619877287
Mean74.62340764
Median Absolute Deviation (MAD)20
Skewness-0.02261952787
Sum46863.5
Variance675.1741402
MonotonicityNot monotonic
2022-12-16T17:32:51.707547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8215
 
2.3%
9213
 
2.0%
10012
 
1.8%
8112
 
1.8%
5711
 
1.7%
10111
 
1.7%
7211
 
1.7%
10811
 
1.7%
7611
 
1.7%
4410
 
1.5%
Other values (104)511
77.3%
(Missing)33
 
5.0%
ValueCountFrequency (%)
151
 
0.2%
182
 
0.3%
192
 
0.3%
201
 
0.2%
211
 
0.2%
222
 
0.3%
241
 
0.2%
251
 
0.2%
25.51
 
0.2%
285
0.8%
ValueCountFrequency (%)
1501
 
0.2%
1491
 
0.2%
1441
 
0.2%
1401
 
0.2%
1351
 
0.2%
1331
 
0.2%
1301
 
0.2%
1291
 
0.2%
1283
0.5%
1271
 
0.2%

QD_R
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct121
Distinct (%)19.3%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean81.78264331
Minimum14
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:51.880959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile35.35
Q161
median82
Q3100
95-th percentile127
Maximum166
Range152
Interquartile range (IQR)39

Descriptive statistics

Standard deviation27.32016976
Coefficient of variation (CV)0.3340582874
Kurtosis-0.4271409042
Mean81.78264331
Median Absolute Deviation (MAD)19
Skewness0.05279084141
Sum51359.5
Variance746.3916759
MonotonicityNot monotonic
2022-12-16T17:32:52.033240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7213
 
2.0%
8113
 
2.0%
8213
 
2.0%
8913
 
2.0%
7813
 
2.0%
10012
 
1.8%
9212
 
1.8%
8412
 
1.8%
8010
 
1.5%
9610
 
1.5%
Other values (111)507
76.7%
(Missing)33
 
5.0%
ValueCountFrequency (%)
141
 
0.2%
181
 
0.2%
201
 
0.2%
252
0.3%
262
0.3%
271
 
0.2%
283
0.5%
291
 
0.2%
312
0.3%
324
0.6%
ValueCountFrequency (%)
1661
0.2%
1551
0.2%
1501
0.2%
1482
0.3%
1431
0.2%
1421
0.2%
1401
0.2%
1382
0.3%
1372
0.3%
1362
0.3%

VABS_Com
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct92
Distinct (%)17.6%
Missing137
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean63.79770992
Minimum20
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:52.201826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q148
median65
Q380
95-th percentile96.85
Maximum121
Range101
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.34522107
Coefficient of variation (CV)0.350251147
Kurtosis-0.6024498023
Mean63.79770992
Median Absolute Deviation (MAD)16
Skewness-0.1881658652
Sum33430
Variance499.3089049
MonotonicityNot monotonic
2022-12-16T17:32:52.362689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2033
 
5.0%
6215
 
2.3%
8014
 
2.1%
7914
 
2.1%
7412
 
1.8%
7511
 
1.7%
9211
 
1.7%
8511
 
1.7%
7711
 
1.7%
4410
 
1.5%
Other values (82)382
57.8%
(Missing)137
 
20.7%
ValueCountFrequency (%)
2033
5.0%
211
 
0.2%
243
 
0.5%
252
 
0.3%
262
 
0.3%
274
 
0.6%
282
 
0.3%
293
 
0.5%
301
 
0.2%
312
 
0.3%
ValueCountFrequency (%)
1211
0.2%
1181
0.2%
1141
0.2%
1131
0.2%
1121
0.2%
1081
0.2%
1072
0.3%
1062
0.3%
1051
0.2%
1041
0.2%

VABS_Soc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)13.7%
Missing135
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean66.18441065
Minimum20
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:52.838961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31
Q155
median67
Q378
95-th percentile94
Maximum106
Range86
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.54315019
Coefficient of variation (CV)0.2650646884
Kurtosis0.4238156378
Mean66.18441065
Median Absolute Deviation (MAD)11
Skewness-0.5099935713
Sum34813
Variance307.7621184
MonotonicityNot monotonic
2022-12-16T17:32:52.993693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020
 
3.0%
7818
 
2.7%
5817
 
2.6%
6917
 
2.6%
7116
 
2.4%
6816
 
2.4%
6416
 
2.4%
5214
 
2.1%
6614
 
2.1%
7314
 
2.1%
Other values (62)364
55.1%
(Missing)135
 
20.4%
ValueCountFrequency (%)
2020
3.0%
222
 
0.3%
242
 
0.3%
281
 
0.2%
313
 
0.5%
341
 
0.2%
351
 
0.2%
361
 
0.2%
381
 
0.2%
404
 
0.6%
ValueCountFrequency (%)
1061
 
0.2%
1042
 
0.3%
1011
 
0.2%
1001
 
0.2%
991
 
0.2%
975
0.8%
964
0.6%
958
1.2%
945
0.8%
937
1.1%

VABS_Aut
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct76
Distinct (%)14.5%
Missing137
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean60.46564885
Minimum20
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:53.152282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q151
median63
Q375
95-th percentile87
Maximum111
Range91
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.40010357
Coefficient of variation (CV)0.3208450407
Kurtosis-0.2258103218
Mean60.46564885
Median Absolute Deviation (MAD)12
Skewness-0.5771709256
Sum31684
Variance376.3640185
MonotonicityNot monotonic
2022-12-16T17:32:53.302822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2044
 
6.7%
6419
 
2.9%
7517
 
2.6%
7215
 
2.3%
6315
 
2.3%
6614
 
2.1%
6014
 
2.1%
5414
 
2.1%
7913
 
2.0%
6213
 
2.0%
Other values (66)346
52.3%
(Missing)137
 
20.7%
ValueCountFrequency (%)
2044
6.7%
221
 
0.2%
231
 
0.2%
243
 
0.5%
252
 
0.3%
261
 
0.2%
271
 
0.2%
282
 
0.3%
293
 
0.5%
301
 
0.2%
ValueCountFrequency (%)
1111
 
0.2%
1021
 
0.2%
1011
 
0.2%
972
0.3%
961
 
0.2%
952
0.3%
941
 
0.2%
931
 
0.2%
914
0.6%
894
0.6%

ADIR_Soc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)4.7%
Missing23
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean18.97335423
Minimum0
Maximum30
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:53.440979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q114
median19
Q325
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.519930442
Coefficient of variation (CV)0.3436361522
Kurtosis-0.903112607
Mean18.97335423
Median Absolute Deviation (MAD)5
Skewness-0.05696923026
Sum12105
Variance42.50949297
MonotonicityNot monotonic
2022-12-16T17:32:53.562476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1641
 
6.2%
2837
 
5.6%
1137
 
5.6%
1436
 
5.4%
2235
 
5.3%
1334
 
5.1%
2634
 
5.1%
1532
 
4.8%
1732
 
4.8%
1031
 
4.7%
Other values (20)289
43.7%
ValueCountFrequency (%)
01
 
0.2%
11
 
0.2%
22
 
0.3%
41
 
0.2%
52
 
0.3%
62
 
0.3%
78
 
1.2%
85
 
0.8%
96
 
0.9%
1031
4.7%
ValueCountFrequency (%)
3029
4.4%
2916
2.4%
2837
5.6%
2715
2.3%
2634
5.1%
2530
4.5%
2430
4.5%
2328
4.2%
2235
5.3%
2124
3.6%

ADIR_RRB
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)2.2%
Missing24
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean4.485086342
Minimum0
Maximum23
Zeros6
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:53.679661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q36
95-th percentile8
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.000435613
Coefficient of variation (CV)0.4460194209
Kurtosis11.71046292
Mean4.485086342
Median Absolute Deviation (MAD)1
Skewness1.899872272
Sum2857
Variance4.001742642
MonotonicityNot monotonic
2022-12-16T17:32:53.788721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3178
26.9%
4162
24.5%
591
13.8%
674
11.2%
835
 
5.3%
735
 
5.3%
229
 
4.4%
110
 
1.5%
96
 
0.9%
106
 
0.9%
Other values (4)11
 
1.7%
(Missing)24
 
3.6%
ValueCountFrequency (%)
06
 
0.9%
110
 
1.5%
229
 
4.4%
3178
26.9%
4162
24.5%
591
13.8%
674
11.2%
735
 
5.3%
835
 
5.3%
96
 
0.9%
ValueCountFrequency (%)
231
 
0.2%
122
 
0.3%
112
 
0.3%
106
 
0.9%
96
 
0.9%
835
 
5.3%
735
 
5.3%
674
11.2%
591
13.8%
4162
24.5%

ADIR_AbDev
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)1.3%
Missing32
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean4.027027027
Minimum0
Maximum25
Zeros20
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:32:53.893812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.562162341
Coefficient of variation (CV)0.3879195075
Kurtosis51.4978199
Mean4.027027027
Median Absolute Deviation (MAD)1
Skewness2.994692219
Sum2533
Variance2.440351179
MonotonicityNot monotonic
2022-12-16T17:32:53.999353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5295
44.6%
4161
24.4%
387
 
13.2%
233
 
5.0%
126
 
3.9%
020
 
3.0%
66
 
0.9%
251
 
0.2%
(Missing)32
 
4.8%
ValueCountFrequency (%)
020
 
3.0%
126
 
3.9%
233
 
5.0%
387
 
13.2%
4161
24.4%
5295
44.6%
66
 
0.9%
251
 
0.2%
ValueCountFrequency (%)
251
 
0.2%
66
 
0.9%
5295
44.6%
4161
24.4%
387
 
13.2%
233
 
5.0%
126
 
3.9%
020
 
3.0%

Interactions

2022-12-16T17:32:48.034330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:33.509071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.055771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.477848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.050375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.486922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:40.914916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.468385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.765490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:45.089479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.463846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:48.157557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:33.654628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.190088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.609485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.177964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.625098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:41.038192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.585334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.893567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:45.214998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.585037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:48.284301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:33.912451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.328528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.745100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.310791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.763309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:41.167784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.707583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.032188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:45.350670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.965118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:48.414917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.046936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.462863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.886169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.446387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.898077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:41.299846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.832091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.166313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-16T17:32:48.542676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.184720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.594578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.016736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.580545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:40.031455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:41.421661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.951259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.288790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:45.608775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:47.213402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:48.674416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.329224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.729509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.151576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:38.735165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-16T17:32:41.754177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.076590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.406560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:45.738035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-16T17:32:48.791611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.447858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.853065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-16T17:32:47.453594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:48.905335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.566983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:35.972461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.542448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-16T17:32:49.029832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.681296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.093168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.664452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.120093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:40.527706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.111181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.418792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.750085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.087140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:47.678800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:49.165383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.810685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.221980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.797065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.247771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:40.657494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.232947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.535979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.867111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.212488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:47.806681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:49.293764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:34.930513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:36.348525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:37.922406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:39.365142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:40.788991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:42.346885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:43.644218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:44.976164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:46.334018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:32:47.917867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-16T17:32:54.115547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-16T17:32:54.311878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-16T17:32:54.520810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-16T17:32:54.712469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-16T17:32:49.501164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-16T17:32:49.762082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-16T17:32:49.998503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-16T17:32:50.236152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CodeQD_MQD_PSQD_LQD_EHQD_RVABS_ComVABS_SocVABS_AutADIR_SocADIR_RRBADIR_AbDev
0AA147.035.013.028.038.0NaNNaNNaN28.09.02.0
1AA3443.027.018.025.031.0NaNNaNNaN30.08.05.0
2AA3652.035.08.031.045.0NaNNaNNaN30.08.05.0
3AA7229.021.023.021.020.0NaNNaNNaN26.04.05.0
4AA9128.019.011.018.026.0NaNNaNNaN28.08.04.0
5AA271.043.030.038.042.020.020.020.027.04.01.0
6AA362.038.028.047.038.0NaNNaNNaN30.05.03.0
7AA693.087.074.055.084.040.050.054.013.03.04.0
8AA797.070.039.051.080.020.041.020.024.04.03.0
9AA843.029.022.029.014.0NaNNaNNaN29.04.05.0

Last rows

CodeQD_MQD_PSQD_LQD_EHQD_RVABS_ComVABS_SocVABS_AutADIR_SocADIR_RRBADIR_AbDev
651AA906NaNNaN0.0NaNNaN62.066.058.021.05.05.0
652AA90388.058.035.063.0100.0NaNNaNNaN9.05.02.0
653AA90089.078.047.092.078.0NaNNaNNaN11.03.05.0
654AA92298.090.095.092.095.069.072.069.07.05.04.0
655AA90473.052.042.069.069.066.068.067.0NaNNaNNaN
656AA92776.055.084.081.084.079.074.079.0NaNNaNNaN
657AA93897.073.065.077.097.078.068.077.011.03.0NaN
658AA93458.040.020.031.034.047.063.055.010.03.025.0
659AA942115.085.047.089.085.063.077.069.0NaNNaNNaN
660AA83780.062.064.083.083.072.059.058.015.03.05.0